“An extensive multisensor hyperspectral benchmark datasets of intimate mixtures of mineral powders”. Koirala B, Rasti B, Bnoulkacem Z, De Lima Ribeiro A, Madriz Y, Herrmann E, Gestels A, De Kerf T, Janssens K, Steenackers G, Gloaguen R, Scheunders P, IEEE International Geoscience and Remote Sensing Symposium proceedings
T2 –, IGARSS 2023 –, 2023 IEEE International Geoscience and Remote Sensing Symposium, 16-21 July 2023, Pasadena, CA, USA , 5890 (2023). http://doi.org/10.1109/IGARSS52108.2023.10281467
Abstract: Since many materials behave as heterogeneous intimate mixtures with which each photon interacts differently, the relationship between spectral reflectance and material composition is very complex. Quantitative validation of spectral unmixing algorithms requires high-quality ground truth fractional abundance data, which are very difficult to obtain.In this work, we generated a comprehensive hyperspectral dataset of intimate mineral powder mixtures by homogeneously mixing five different clay powders (Kaolin, Roof clay, Red clay, mixed clay, and Calcium hydroxide). In total 325 samples were prepared. Among the 325 samples, 60 mixtures were binary, 150 were ternary, 100 were quaternary, and 15 were quinary. For each mixture (and pure clay powder), reflectance spectra are acquired by 13 different sensors, with a broad wavelength range between the visible and the long-wavelength infrared regions (i.e., between 350 nm and 15385 nm) and with a large variation in sensor types, platforms, and acquisition conditions. We will make this dataset public, to be used by the community for the validation of nonlinear unmixing methodologies (https://github.com/VisionlabUA/Multisensor_datasets)
Keywords: P1 Proceeding; Economics; Vision lab; Antwerp X-ray Imaging and Spectroscopy (AXIS)
DOI: 10.1109/IGARSS52108.2023.10281467
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“A multisensor hyperspectral benchmark dataset for unmixing of intimate mixtures”. Koirala B, Rasti B, Bnoulkacem Z, de Lima Ribeiro A, Madriz Y, Herrmann E, Gestels A, De Kerf T, Lorenz S, Fuchs M, Janssens K, Steenackers G, Gloaguen R, Scheunders P, IEEE sensors journal 24, 4694 (2024). http://doi.org/10.1109/JSEN.2023.3343552
Abstract: Optical hyperspectral cameras capture the spectral reflectance of materials. Since many materials behave as heterogeneous intimate mixtures with which each photon interacts differently, the relationship between spectral reflectance and material composition is very complex. Quantitative validation of spectral unmixing algorithms requires high-quality ground truth fractional abundance data, which are very difficult to obtain. In this work, we generated a comprehensive laboratory ground truth dataset of intimately mixed mineral powders. For this, five clay powders (Kaolin, Roof clay, Red clay, mixed clay, and Calcium hydroxide) were mixed homogeneously to prepare 325 samples of 60 binary, 150 ternary, 100 quaternary, and 15 quinary mixtures. Thirteen different hyperspectral sensors have been used to acquire the reflectance spectra of these mixtures in the visible, near, short, mid, and long-wavelength infrared regions (350-15385) nm. Overlaps in wavelength regions due to the operational ranges of each sensor and variations in acquisition conditions resulted in a large amount of spectral variability. Ground truth composition is given by construction, but to verify that the generated samples are sufficiently homogeneous, XRD and XRF elemental analysis is performed. We believe these data will be beneficial for validating advanced methods for nonlinear unmixing and material composition estimation, including studying spectral variability and training supervised unmixing approaches. The datasets can be downloaded from the following link: https://github.com/VisionlabHyperspectral/Multisensor_datasets.
Keywords: A1 Journal article; Engineering sciences. Technology; Vision lab; Antwerp X-ray Imaging and Spectroscopy (AXIS)
Impact Factor: 4.3
DOI: 10.1109/JSEN.2023.3343552
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